How to compare and drop rows within groupby in pandas?

Question

I have a data frame that looks like this:

              datetime                     policyid                   score

0   1970-01-01 00:00:01.593560812         9876policyID1234567890        0 

1   1970-01-01 00:00:01.593560814         9876policyID1234567890        0 

2   1970-01-01 00:00:01.593560958         9876policyID1234567890        1

3   1970-01-01 00:00:01.593560964         9876policyID1234567890        1

I want to group by policyid and score BUT only keep the row with the greatest stamp per the same policyid and score.

I am doing the groupby like so:

df.groupby(['policyid','score'])

At this point, I am not sure how to compare the timestamp between rows and keep the row with the greater time stamp.

New data frame should look like this:

              datetime                     policyid                   score

1   1970-01-01 00:00:01.593560814         9876policyID1234567890        0 

3   1970-01-01 00:00:01.593560964         9876policyID1234567890        1

Thank you in advance.

Answer 1

You can use sort_values, then drop_duplicates:

df=df.sort_values('datetime').drop_duplicates(['policyid','score'], keep='last') 

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